In [ ]:
import pandas as pd
from matplotlib import pyplot as plt

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
from sklearn.decomposition import PCA

import seaborn as sns
import numpy as np
In [ ]:
df = pd.read_csv('../../datasets/wine.csv')
df.head()
Out[ ]:
Wine Alcohol Malic.acid Ash Acl Mg Phenols Flavanoids Nonflavanoid.phenols Proanth Color.int Hue OD Proline
0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065
1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050
2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185
3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480
4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735
In [ ]:
df.describe()
Out[ ]:
Wine Alcohol Malic.acid Ash Acl Mg Phenols Flavanoids Nonflavanoid.phenols Proanth Color.int Hue OD Proline
count 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000 178.000000
mean 1.938202 13.000618 2.336348 2.366517 19.494944 99.741573 2.295112 2.029270 0.361854 1.590899 5.058090 0.957449 2.611685 746.893258
std 0.775035 0.811827 1.117146 0.274344 3.339564 14.282484 0.625851 0.998859 0.124453 0.572359 2.318286 0.228572 0.709990 314.907474
min 1.000000 11.030000 0.740000 1.360000 10.600000 70.000000 0.980000 0.340000 0.130000 0.410000 1.280000 0.480000 1.270000 278.000000
25% 1.000000 12.362500 1.602500 2.210000 17.200000 88.000000 1.742500 1.205000 0.270000 1.250000 3.220000 0.782500 1.937500 500.500000
50% 2.000000 13.050000 1.865000 2.360000 19.500000 98.000000 2.355000 2.135000 0.340000 1.555000 4.690000 0.965000 2.780000 673.500000
75% 3.000000 13.677500 3.082500 2.557500 21.500000 107.000000 2.800000 2.875000 0.437500 1.950000 6.200000 1.120000 3.170000 985.000000
max 3.000000 14.830000 5.800000 3.230000 30.000000 162.000000 3.880000 5.080000 0.660000 3.580000 13.000000 1.710000 4.000000 1680.000000
In [ ]:
df.isnull().sum()
Out[ ]:
Wine                    0
Alcohol                 0
Malic.acid              0
Ash                     0
Acl                     0
Mg                      0
Phenols                 0
Flavanoids              0
Nonflavanoid.phenols    0
Proanth                 0
Color.int               0
Hue                     0
OD                      0
Proline                 0
dtype: int64
In [ ]:
sc = StandardScaler()
scaled_data = df.copy()
scaled_data = sc.fit_transform(scaled_data)
df
Out[ ]:
Wine Alcohol Malic.acid Ash Acl Mg Phenols Flavanoids Nonflavanoid.phenols Proanth Color.int Hue OD Proline
0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065
1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050
2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185
3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480
4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
173 3 13.71 5.65 2.45 20.5 95 1.68 0.61 0.52 1.06 7.70 0.64 1.74 740
174 3 13.40 3.91 2.48 23.0 102 1.80 0.75 0.43 1.41 7.30 0.70 1.56 750
175 3 13.27 4.28 2.26 20.0 120 1.59 0.69 0.43 1.35 10.20 0.59 1.56 835
176 3 13.17 2.59 2.37 20.0 120 1.65 0.68 0.53 1.46 9.30 0.60 1.62 840
177 3 14.13 4.10 2.74 24.5 96 2.05 0.76 0.56 1.35 9.20 0.61 1.60 560

178 rows × 14 columns

In [ ]:
# Reducing the Number of Features in the Dataset using PCA
pca = PCA(2)
pca_data = pca.fit_transform(df)
In [ ]:
plt.rcParams["figure.figsize"] = (12, 8)
plt.rcParams["figure.dpi"] = 80
plt.scatter(x=pca_data[:,0], y=pca_data[:,1], lw=2)
plt.xlabel("Principle Component 1")
plt.ylabel("Principle Component 2")
plt.title("Principle Components Analysis")
plt.show()
In [ ]:
wcss = []

for i in range(1, 11):
    kmeans = KMeans(
        n_clusters = i,
        init = 'k-means++',
        random_state=42
    )
    kmeans.fit(df)
    wcss.append([i, kmeans.inertia_]) # kmeans.inertial_ returns the calculated WCSS Values
    
wcss_dataframe = pd.DataFrame(wcss, columns=["clusters", "wcss value"])

# Plot for Elbow Method
plt.rcParams["figure.figsize"] = (12, 8)
plt.rcParams["figure.dpi"] = 80
sns.lineplot(
    x = wcss_dataframe.clusters.values,
    y = wcss_dataframe["wcss value"], marker="o")
plt.xticks(np.arange(1, 11))
plt.xlabel("Clusters")
plt.ylabel("Wcss Values")
plt.title("Elbow Method Plot")
plt.show()

c:\Users\adity\anaconda3\envs\ml\lib\site-packages\sklearn\cluster\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [ ]:
kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 42)
y_kmeans = pd.Series(kmeans.fit_predict(df))
y_kmeans
Out[ ]:
0      1
1      1
2      1
3      1
4      2
      ..
173    2
174    2
175    2
176    2
177    0
Length: 178, dtype: int32
In [ ]:
y_kmeans.value_counts()
Out[ ]:
0    69
2    62
1    47
dtype: int64
In [ ]:
centroids = kmeans.cluster_centers_
centroids_pca = pca.transform(centroids)

c:\Users\adity\anaconda3\envs\ml\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names
  warnings.warn(
In [ ]:
pca_dataframe = pd.DataFrame(pca_data, columns=["PCA 1", "PCA 2"])
pca_dataframe["Cluster"] = y_kmeans
pca_dataframe.sample(10)
Out[ ]:
PCA 1 PCA 2 Cluster
107 -259.148149 -11.079721 0
159 -127.062791 -8.288856 2
165 -227.079224 -7.591307 0
167 -62.086591 -10.551195 2
144 108.310890 10.354766 2
65 -68.905773 -0.558779 2
18 933.118716 -8.352965 1
14 800.053884 -12.117564 1
59 -227.032511 -7.995881 0
29 288.009360 -8.961092 1
In [ ]:
plt.rcParams["figure.figsize"] = (12, 8)
plt.rcParams["figure.dpi"] = 80
sns.scatterplot(x="PCA 1", y="PCA 2", hue="Cluster", data=pca_dataframe, palette=['green','orange','brown'], s=100)
plt.scatter(x=centroids_pca[:, 0], y=centroids_pca[:, 1], marker="+", s=500, linewidths=3, lw=4, color="blue", zorder=10)
plt.title("Visualizing Clusters")
plt.show()
In [ ]:
df["Cluster"] = y_kmeans
df.head()
Out[ ]:
Wine Alcohol Malic.acid Ash Acl Mg Phenols Flavanoids Nonflavanoid.phenols Proanth Color.int Hue OD Proline Cluster
0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065 1
1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050 1
2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185 1
3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480 1
4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735 2
In [ ]:
sns.pairplot(df, hue="Cluster", palette=['green','orange','brown'])
plt.show()
In [ ]: